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> For me, it helps organize a lot of well-known algorithms as special cases of a general framework—which is worthwhile in itself.

Which well known algorithms do you have in mind here?

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For a flavor, see Tom Minka’s recent talk/slides “From automatic differentiation to message passing”.

Difficult to give a quick answer. I’m also not aware of any good resources where this is spelled out. If you’re seriously interested, feel free to hit me up for a deeper discussion.

Thanks I’ll check that out. My own understanding is that pretty much any probabilistic graphical model can be constructed as a probabilistic program, combined with pretty much any mode of inference. How such programs compare to specialized algorithms in terms of efficiency is not clear to me. I’m asking because my understanding is based mostly on theory and I’d like to learn more about probabilistic programming in practice.

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